Ricerc@Sapienza

In this paper we quantify the contribution to systemic risk of a single financial institution by utilizing a analytical framework based on the principles of Extreme Value Theory (EVT) for modelling the marginal distributions and on the properties of copula functions for describing the dependence structure between the financial system and the single financial institution.

This work is intended to assess the contribution to systemic risk of major companies
in the European stock market on a geographical basis. We use the EuroStoxx 50
Index as a proxy for the financial system and we rely on the CoVaR and Delta-CoVaR risk
measures to estimate the contribution of each European country belonging to the index to
systemic risk. We also conduct the significance and dominance test to evaluate whether
the systemic relevance of considered countries is statistically significant and to determine

We propose a recursive partitioning approach to identify groups of risky financial institutions using a synthetic indicator built on the information arising from a sample of pooled systemic risk measures. The composition and amplitude of the risky groups change over time, emphasizing the periods of high systemic risk stress. We also calculate the probability that a financial institution can change risk group over the next month and show that a firm belonging to the lowest or highest risk group has in general a high probability to remain in that group.

This paper proposes an optimal allocation model with the main aim to minimize systemic risk related to the sovereign risk of a set of countries. The reference methodological environment is that of complex networks theory. Specifically, we consider the weighted clustering coefficient as a proxy of systemic risk, while the interconnections among countries are captured by the relationships among default probabilities of the set of countries under consideration. The selected optimization criterion is based on minimization of the mean absolute deviation.

In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order l of a node i introduced in Cerqueti et al. (2018). Its properties are also explored in terms of systemic risk assessment.